Continuous Indoor Positioning Using GNSS, Wi-Fi, and MEMS Dead Reckoning

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1 Continuous Indoor Positioning Using GNSS, Wi-Fi, and MEMS Dead Reckoning J. Blake Bullock, Mahesh Chowdhary, Dimitri Rubin, Donald Leimer, Greg Turetzky, Murray Jarvis CSR Technology, Inc. ION GNSS 2012 Session F4 Sept 20, 2012 Page 1

2 Market Drivers for Location-Based Services (LBS) LBS is mass market Smartphones all feature GPS to enable consumer LBS High volume and high growth Millions of applications LBS is a daily experience LBS is used for search, mapping, navigation, check-in, fitness, tracking, social networking, etc. (MU) Smartphones Source: CSR Estimates 2012 But... LBS is currently not indoors Consumers expect functionality anywhere anytime Consumers are indoors a very high percentage of the time Page 2

3 Indoor LBS Experiences Micello Find nearest shop in the mall that has the product you are looking for Find train, browse stores and receive ads 3D indoor map with search and walking directions Location check-in with friends Need accurate & continuous indoor positioning Page 3

4 Applications Requiring Accurate Indoor Location Handsets and tablets Search, map, navigation Tracking Social networking Cameras Geotagging Fitness devices Speed, altitude, track Context recognition Asset tracking Low-power tracking Geofencing Alerts Page 4

5 Introducing SiRFusion SiRFusion uses Wi-Fi signals and phone sensors to determine position indoors SiRFusion enables indoor positioning, navigation, and LBS applications SiRFusion also improves performance in outdoor environments Page 5

6 Next Generation Location Platform SiRFstarV architecture with SiRFusion hybrid positioning system CSR Positioning Center Quad GNSS MEMS Gateway SiRFstarV Wireless Sniffer (Wi-Fi, GSM, ) SiRFstarV SiRFusion Pedestrian Dead Reckoning Taking mobile location to the next frontiers Page 6

7 GNSS Positioning SiRFstarV Quad-GNSS tracks: GPS GLONASS WAAS / EGNOS QZSS Galileo / COMPASS (future) All available satellites are tracked and used in solution simultaneously Corrections from SBAS are applied when available Multipath and interference mitigation techniques help coverage and accuracy Sensitivity now down to -165dBm Page 7

8 GNSS Positioning Indoors Receiver SiRFstarV SiRFstarV Mode GPS + GLONASS GPS Only CN0 Avg dbhz Total Samples Fixes Tracking Availability 96.8% 96.8% Satellite Number Avg Cross Track Error 50% m Cross Track Error 95% m Altitude Error 50% m Altitude Error 95% m Three story commercial building Very high availability 96.8% GLONASS improves accuracy 7-15% More than half of the fixes are outside the building GLONASS helps indoors, but not enough Page 8

9 MEMS Pedestrian Dead Reckoning 10-DOF MEMS: 3-axis accelerometer 3-axis magnetometer 3-axis gyro Barometer Input signals are synchronized and buffered Continuous calibration employed Context detection used to determine when to invoke pedestrian mode Walking, running, standing, stairs, escalator, elevator, etc. Pedestrian dead reckoning used to determine path and speed by step counting rather than integrating acceleration Error growth with no absolute position is about 10% of distance traveled Suitable for smoothing and bridging between absolute fixes Page 9

10 Wi-Fi Positioning SiRFstarV has Wi-Fi sniffers to determine access point (AP) broadcast IDs (BSSIDs) and signal strength (RSSI) Positioning is based on AP location and requires knowledge of the AP centroid locations AP data delivered to mobile devices and cached for local position determination Self-learning system uses harvested Wi-Fi data to learn AP locations automatically No new infrastructure required; millions deployed worldwide No manual surveying required; self-learning system GNSS position + MEMS PDR indoors used to initially learn indoor APs Performance varies with location and time, especially in dynamic environments with many pedestrians Page 10

11 Platform {PR, DR} {, } {Alt GPS } {CNo} {PR} Fusion of GNSS and Hybrid Sensors Available Measurements PDR GNSS WiFi GSM Baro PDR 50 Hz 10 Hz GNSS Only WiFi GSM Baro PDR Fusion PDR SDS Dynamic Detect {P,V,T} {context} Context Aware {ΔP, P, Q} NAV FILTER {, } {Alt BARO,Q} Nav State Estimate Page 11

12 SiRFusion Test Platform Module with SiRFstarV and 10-DOF MEMS sensor suite Integrated in an HTC Google Nexus One phone running SiRFusion Page 12

13 SiRFusion Test Tokyo Station Lower level at Tokyo station in shopping area, two levels below tracks Indoor map shown on Google Earth Environment has no GNSS signal, lots of magnetic anomaly sources (tracks, elevators, escalators), and many people causing signal variations Wi-Fi solution has jitter and discontinuities PDR solution deviates from true path SiRFusion combined solution nearest truth Path walked Wi-Fi positioning MEMS PDR SiRFusion Page 13

14 SiRFusion Test Tokyo Station First fix determined rapidly and accurately SiRFusion detects each turn correctly Total duration 6 minutes Largest error about 7m Path walked SiRFusion Page 14

15 SiRFusion Test Tokyo Station SiRFusion detects each turn correctly Total duration 7 minutes Largest error about 5m Path walked SiRFusion Page 15

16 SiRFusion Test Tokyo Station Path of three separate test runs Cold start before each test Very repeatable results Path walked SiRFusion Page 16

17 SiRFusion Test Accuracy Confidence level 50% CEP % CEP % CEP 13.1 Cross track error (m) Accuracy test over several test runs in shopping mall Statistics computed using several established test points indoors Page 17

18 Conclusions SiRFusion determines continuous position indoors and through transition areas No additional infrastructure to install No manual surveys to perform Self-learning adapts to changing environment Position availability approaches 100% Accuracy exceeds 5m 50% (CEP) Suitable for integration in handsets, cameras, tracking systems, and other small electronic devices Continuous indoor positioning enables indoor LBS Page 18

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